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#!/usr/bin/env python3
from collections import Counter
from itertools import islice
from typing import Any, Iterator, List, Mapping
def shard(lst: List[Any], size: int) -> Iterator[Any]:
"""
Yield successive size-sized shards from lst.
>>> for sublist in shard([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 3):
... [_ for _ in sublist]
[1, 2, 3]
[4, 5, 6]
[7, 8, 9]
[10, 11, 12]
"""
for x in range(0, len(lst), size):
yield islice(lst, x, x + size)
def flatten(lst: List[Any]) -> List[Any]:
"""
Flatten out a list:
>>> flatten([ 1, [2, 3, 4, [5], 6], 7, [8, [9]]])
[1, 2, 3, 4, 5, 6, 7, 8, 9]
"""
if len(lst) == 0:
return lst
if isinstance(lst[0], list):
return flatten(lst[0]) + flatten(lst[1:])
return lst[:1] + flatten(lst[1:])
def prepend(item: Any, lst: List[Any]) -> List[Any]:
"""
Prepend an item to a list.
>>> prepend('foo', ['bar', 'baz'])
['foo', 'bar', 'baz']
"""
lst.insert(0, item)
return lst
def population_counts(lst: List[Any]) -> Mapping[Any, int]:
"""
Return a population count mapping for the list (i.e. the keys are
list items and the values are the number of occurrances of that
list item in the original list.
>>> population_counts([1, 1, 1, 2, 2, 3, 3, 3, 4])
Counter({1: 3, 3: 3, 2: 2, 4: 1})
"""
return Counter(lst)
def most_common_item(lst: List[Any]) -> Any:
"""
Return the most common item in the list. In the case of ties,
which most common item is returned will be random.
>>> most_common_item([1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4])
3
"""
return population_counts(lst).most_common(1)[0][0]
def least_common_item(lst: List[Any]) -> Any:
"""
Return the least common item in the list. In the case of
ties, which least common item is returned will be random.
>>> least_common_item([1, 1, 1, 2, 2, 3, 3, 3, 4])
4
"""
return population_counts(lst).most_common()[-1][0]
def dedup_list(lst: List[Any]) -> List[Any]:
"""
Remove duplicates from the list performantly.
>>> dedup_list([1, 2, 1, 3, 3, 4, 2, 3, 4, 5, 1])
[1, 2, 3, 4, 5]
"""
return list(set(lst))
if __name__ == '__main__':
import doctest
doctest.testmod()
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